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Related Experiment Video

Updated: Jan 14, 2026

Applying Incongruent Visual-Tactile Stimuli during Object Transfer with Vibro-Tactile Feedback
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Data: WireFishing-M: A multimodal dataset for deformable cable insertion using tactile, visual, and proprioceptive

Tianyu Zhou1, Hengxu You1, Fang Xu1

  • 1The Informatics, Cobots and Intelligent Construction (ICIC) Lab, University of Florida, 1949 Stadium Rd, Gainesville, FL 32611, USA.

Data in Brief
|October 27, 2025
PubMed
Summary

This study introduces WireFishing-M, a multimodal dataset for robotic manipulation research. It features cable insertion tasks using tactile sensing and multi-view video for developing advanced robotic control.

Keywords:
Deformable object manipulationMultimodal datasetRobotic wire insertionTactile sensing

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Area of Science:

  • Robotics
  • Computer Vision
  • Machine Learning

Background:

  • Deformable object manipulation and tactile sensing are crucial for advanced robotic capabilities.
  • Existing datasets often lack multimodal data for complex contact-rich tasks.

Purpose of the Study:

  • Introduce WireFishing-M, a novel multimodal dataset for robotic wire insertion.
  • Support research in deformable object manipulation, tactile sensing, and robotic learning.
  • Provide a benchmark for multimodal model development in robotics.

Main Methods:

  • Utilized a Franka Emika Panda robot with an Allegro Hand and DIGIT tactile sensor.
  • Captured synchronized GelSight tactile images, multi-view RGB videos, and end-effector poses.
  • Included seven cable types with varying physical properties for diverse insertion scenarios.

Main Results:

  • The dataset comprises comprehensive synchronized sensor data, including tactile, visual, and pose information.
  • WireFishing-M facilitates the study of contact interactions during robotic cable insertion.
  • Data is organized for direct use in robotic learning and simulation frameworks.

Conclusions:

  • WireFishing-M is a valuable resource for advancing research in robotic manipulation and tactile sensing.
  • The dataset enables the development and benchmarking of multimodal AI models for complex robotic tasks.
  • Public availability promotes wider adoption and accelerates progress in the field.